The described related work tracks motion using optical flow algorithms. It seems that those produce satisfying results but not yet cover the full potential of a AR tracking system. Others use interest-point based algorithms which are commonly known as very computational expensive. SIFT descriptors are probably the most used ones although they might belong the the most expensive ones. Nevertheless, some improvements have been achieved with SIFT and also SURF algorithms.
Similar to PTAM Wagner et. al also use a separated detection and tracking system. The detection system tries to find known targets in the currently available camera image using a modified SIFT algorithm. Instead of calculating the kind of expensive Differences of Gaussian (DoG) they use a FAST corner detection over multiple scales. Memory consumption is then reduced by using only 36-dimensional features instead of the original 128-dimensions of SIFT. Found descriptors are matched with entries from multiple spill trees, which is a similar data structure like the k-d-tree used in the original SIFT.
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